store = {}
store['args']={'name': 'bald_mnist_107856', 'type': 'AcquisitionFunction.bald', 'seed': 107856, 'experiment_description': 'Coreset BALD vs BALD', 'acquisition_method': 'AcquisitionMethod.independent', 'available_sample_k': 1, 'num_inference_samples': 20, 'batch_size': 64, 'scoring_batch_size': 512, 'test_batch_size': 512, 'validation_set_size': 1024, 'early_stopping_patience': 3, 'epochs': 30, 'epoch_samples': 5056, 'target_accuracy': 0.96, 'target_num_acquired_samples': 300, 'log_interval': 20, 'dataset': 'DatasetEnum.mnist', 'initial_samples': [38043, 40091, 17418, 2094, 39879, 3133, 5011, 40683, 54379, 24287, 9849, 59305, 39508, 39356, 8758, 52579, 13655, 7636, 21562, 41329], 'experiment_task_id': 8, 'experiments_laaos': './experiment_configs/coreset_bald_vs_bald/configs.py', 'no_cuda': False, 'quickquick': False, 'initial_samples_per_class': 2}
store['cmdline']=['./src/ignite_mnist.py', '--experiment_task_id=8', '--experiments_laaos=./experiment_configs/coreset_bald_vs_bald/configs.py']
store['iterations']=[]
store['initial_samples']=[38043, 40091, 17418, 2094, 39879, 3133, 5011, 40683, 54379, 24287, 9849, 59305, 39508, 39356, 8758, 52579, 13655, 7636, 21562, 41329]
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.6738, 'nll': 2.324293111038208}, 'chosen_samples': ['7262'], 'chosen_samples_score': [1.2775082678103558], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.6577, 'nll': 2.525256962966919}, 'chosen_samples': ['55122'], 'chosen_samples_score': [1.23924046529222], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.6725, 'nll': 2.2397888896942137}, 'chosen_samples': ['43060'], 'chosen_samples_score': [1.2249429298702306], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.6808, 'nll': 2.041878442764282}, 'chosen_samples': ['20700'], 'chosen_samples_score': [1.161461274020779], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7302, 'nll': 1.6912929637908936}, 'chosen_samples': ['28391'], 'chosen_samples_score': [1.2295072840462078], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7278, 'nll': 1.6672692951202392}, 'chosen_samples': ['28'], 'chosen_samples_score': [1.1488409453614903], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7385, 'nll': 1.6748211769104004}, 'chosen_samples': ['59446'], 'chosen_samples_score': [1.2214383070799915], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7089, 'nll': 1.852236735534668}, 'chosen_samples': ['34205'], 'chosen_samples_score': [1.1726903252631957], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7439, 'nll': 1.5980154373168944}, 'chosen_samples': ['53136'], 'chosen_samples_score': [1.1399763929115028], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7535, 'nll': 1.5681314346313477}, 'chosen_samples': ['14411'], 'chosen_samples_score': [1.2100199954673658], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7567, 'nll': 1.495373435974121}, 'chosen_samples': ['13333'], 'chosen_samples_score': [1.1426876623369095], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7751, 'nll': 1.3615676645278931}, 'chosen_samples': ['25022'], 'chosen_samples_score': [1.1456848812270182], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7672, 'nll': 1.4021821046829224}, 'chosen_samples': ['31974'], 'chosen_samples_score': [1.1832405437536877], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.776, 'nll': 1.3169630136489867}, 'chosen_samples': ['52582'], 'chosen_samples_score': [1.047221344865295], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7761, 'nll': 1.3409477249145507}, 'chosen_samples': ['42504'], 'chosen_samples_score': [1.103961837900548], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7435, 'nll': 1.5376001541137696}, 'chosen_samples': ['51482'], 'chosen_samples_score': [1.31496153857037], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.78, 'nll': 1.3136838710784913}, 'chosen_samples': ['57507'], 'chosen_samples_score': [1.1864986344705888], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7638, 'nll': 1.4516446668624878}, 'chosen_samples': ['21150'], 'chosen_samples_score': [1.1115829829305899], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7754, 'nll': 1.2689078798294067}, 'chosen_samples': ['59101'], 'chosen_samples_score': [1.1202434707083966], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7697, 'nll': 1.3527277698516846}, 'chosen_samples': ['42092'], 'chosen_samples_score': [1.1183196207709805], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.773, 'nll': 1.3828837409973145}, 'chosen_samples': ['23104'], 'chosen_samples_score': [1.1053811412525514], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7879, 'nll': 1.2226407564163209}, 'chosen_samples': ['19154'], 'chosen_samples_score': [1.126643730975586], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7604, 'nll': 1.4926197467803954}, 'chosen_samples': ['28853'], 'chosen_samples_score': [1.1269670665062912], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7681, 'nll': 1.2713403610229492}, 'chosen_samples': ['6327'], 'chosen_samples_score': [1.0439740314668364], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7683, 'nll': 1.3921091411590576}, 'chosen_samples': ['23041'], 'chosen_samples_score': [1.021883733115276], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7728, 'nll': 1.2266155323028565}, 'chosen_samples': ['26178'], 'chosen_samples_score': [0.9758476913252778], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7528, 'nll': 1.3503709707260132}, 'chosen_samples': ['161'], 'chosen_samples_score': [1.0638475766079702], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.766, 'nll': 1.2882292860031128}, 'chosen_samples': ['50233'], 'chosen_samples_score': [1.0109153927099244], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7748, 'nll': 1.305433820915222}, 'chosen_samples': ['45688'], 'chosen_samples_score': [1.0747952690523106], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7719, 'nll': 1.2601367471694946}, 'chosen_samples': ['48668'], 'chosen_samples_score': [1.0731514970961933], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7891, 'nll': 1.1898748249053954}, 'chosen_samples': ['56356'], 'chosen_samples_score': [1.0777874193714125], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7752, 'nll': 1.234317851638794}, 'chosen_samples': ['39625'], 'chosen_samples_score': [0.9609669735063656], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.779, 'nll': 1.194140644454956}, 'chosen_samples': ['27417'], 'chosen_samples_score': [1.0198924016122506], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7932, 'nll': 1.1674645080566406}, 'chosen_samples': ['39271'], 'chosen_samples_score': [0.9936629261654275], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8285, 'nll': 0.9701383060455322}, 'chosen_samples': ['33388'], 'chosen_samples_score': [0.9574027185737944], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7998, 'nll': 1.064642762374878}, 'chosen_samples': ['45174'], 'chosen_samples_score': [0.94833301057803], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8222, 'nll': 0.9413109878540039}, 'chosen_samples': ['12305'], 'chosen_samples_score': [0.9388405159222161], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8289, 'nll': 1.0170759817123414}, 'chosen_samples': ['15717'], 'chosen_samples_score': [1.1234322978695506], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8081, 'nll': 1.0241700994491578}, 'chosen_samples': ['27441'], 'chosen_samples_score': [0.9990481423458915], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8084, 'nll': 1.0495400588989259}, 'chosen_samples': ['2034'], 'chosen_samples_score': [1.0402803322053158], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8408, 'nll': 0.9037607971191406}, 'chosen_samples': ['12555'], 'chosen_samples_score': [0.9421443445775515], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8, 'nll': 1.0284493616104127}, 'chosen_samples': ['10995'], 'chosen_samples_score': [0.9913388425115044], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8009, 'nll': 1.0017968954086303}, 'chosen_samples': ['52358'], 'chosen_samples_score': [0.8701295802444178], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8124, 'nll': 0.9585281299591064}, 'chosen_samples': ['10028'], 'chosen_samples_score': [0.9081581667268921], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8053, 'nll': 0.9973666257858277}, 'chosen_samples': ['41348'], 'chosen_samples_score': [0.8735269811681873], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8564, 'nll': 0.9038522624969483}, 'chosen_samples': ['17621'], 'chosen_samples_score': [1.0154278325622847], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7993, 'nll': 1.0564218921661377}, 'chosen_samples': ['33593'], 'chosen_samples_score': [0.9170221219361757], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8364, 'nll': 1.0174427320480346}, 'chosen_samples': ['41537'], 'chosen_samples_score': [1.1448701414921527], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8214, 'nll': 1.0115118021011353}, 'chosen_samples': ['22709'], 'chosen_samples_score': [1.0945479602825747], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8629, 'nll': 0.8132673458099365}, 'chosen_samples': ['20449'], 'chosen_samples_score': [1.1315880487952696], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8683, 'nll': 0.813430892753601}, 'chosen_samples': ['11675'], 'chosen_samples_score': [1.113769892247248], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.8674, 'nll': 0.8805376445770263}, 'chosen_samples': ['17324'], 'chosen_samples_score': [1.3103486034768839], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8764, 'nll': 0.7990291058540344}, 'chosen_samples': ['21442'], 'chosen_samples_score': [1.0865649890119027], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8662, 'nll': 0.7729717271804809}, 'chosen_samples': ['20025'], 'chosen_samples_score': [1.067325622078228], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8778, 'nll': 0.7601085879325866}, 'chosen_samples': ['34986'], 'chosen_samples_score': [1.0771865655639647], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8897, 'nll': 0.7170811255455017}, 'chosen_samples': ['2574'], 'chosen_samples_score': [1.095371604739115], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8814, 'nll': 0.7161889007568359}, 'chosen_samples': ['38130'], 'chosen_samples_score': [1.0888431294288137], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8897, 'nll': 0.6925090417861939}, 'chosen_samples': ['991'], 'chosen_samples_score': [1.0743153172003699], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8783, 'nll': 0.787037131690979}, 'chosen_samples': ['38090'], 'chosen_samples_score': [1.2055078751834305], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8774, 'nll': 0.7519992443084716}, 'chosen_samples': ['8958'], 'chosen_samples_score': [1.0423311147294299], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.9063, 'nll': 0.6287938421249389}, 'chosen_samples': ['55274'], 'chosen_samples_score': [1.0538314026947853], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8951, 'nll': 0.6556288631439209}, 'chosen_samples': ['59286'], 'chosen_samples_score': [1.0129358546814373], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.9032, 'nll': 0.677450952720642}, 'chosen_samples': ['38920'], 'chosen_samples_score': [1.1509448176053148], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8937, 'nll': 0.6852225276947022}, 'chosen_samples': ['33674'], 'chosen_samples_score': [1.0227127122394224], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.9038, 'nll': 0.6788004571914673}, 'chosen_samples': ['45212'], 'chosen_samples_score': [1.1519184977556023], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8984, 'nll': 0.6560046821594239}, 'chosen_samples': ['44480'], 'chosen_samples_score': [1.0580937737377971], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8896, 'nll': 0.672159370803833}, 'chosen_samples': ['41295'], 'chosen_samples_score': [0.9858905484339362], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.903, 'nll': 0.7587279504776001}, 'chosen_samples': ['14116'], 'chosen_samples_score': [1.2614882817364101], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.9113, 'nll': 0.598136371421814}, 'chosen_samples': ['10400'], 'chosen_samples_score': [1.1696588636902767], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8919, 'nll': 0.7143739627838135}, 'chosen_samples': ['14821'], 'chosen_samples_score': [1.0554029636372975], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.9065, 'nll': 0.6431174403190613}, 'chosen_samples': ['41324'], 'chosen_samples_score': [1.1517776878169637], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.901, 'nll': 0.6209799335479737}, 'chosen_samples': ['53170'], 'chosen_samples_score': [1.0035342459720074], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.887, 'nll': 0.6725976364135742}, 'chosen_samples': ['20903'], 'chosen_samples_score': [1.0723616145083024], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.9061, 'nll': 0.5977906249046325}, 'chosen_samples': ['33429'], 'chosen_samples_score': [0.9580014452351826], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.9094, 'nll': 0.5994755915641785}, 'chosen_samples': ['24479'], 'chosen_samples_score': [1.1978376316769328], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.9059, 'nll': 0.6089469073295594}, 'chosen_samples': ['424'], 'chosen_samples_score': [1.0601527271861062], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.9008, 'nll': 0.6410114921569824}, 'chosen_samples': ['52086'], 'chosen_samples_score': [0.9752821202255979], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.908, 'nll': 0.6192805027961731}, 'chosen_samples': ['36760'], 'chosen_samples_score': [1.155914345724792], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.887, 'nll': 0.6906634677886963}, 'chosen_samples': ['4061'], 'chosen_samples_score': [1.0424961330062053], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.9079, 'nll': 0.6007285071372985}, 'chosen_samples': ['1075'], 'chosen_samples_score': [1.0530250543560742], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.9166, 'nll': 0.5958999318122864}, 'chosen_samples': ['13829'], 'chosen_samples_score': [1.0784997069857736], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 9, 'test_metrics': {'accuracy': 0.917, 'nll': 0.643083362197876}, 'chosen_samples': ['19590'], 'chosen_samples_score': [1.1958036076397875], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.9067, 'nll': 0.6138903135299683}, 'chosen_samples': ['11645'], 'chosen_samples_score': [1.063167008461559], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8972, 'nll': 0.6642727585792542}, 'chosen_samples': ['50370'], 'chosen_samples_score': [1.013973050968227], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.9162, 'nll': 0.5998281550407409}, 'chosen_samples': ['28102'], 'chosen_samples_score': [1.1986001372811461], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 9, 'test_metrics': {'accuracy': 0.9172, 'nll': 0.6491968480110168}, 'chosen_samples': ['34743'], 'chosen_samples_score': [1.3137315518456774], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.9121, 'nll': 0.6196833573341369}, 'chosen_samples': ['3026'], 'chosen_samples_score': [1.220240247458446], 'chosen_samples_orignal_score': None})
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store['iterations'].append({'num_epochs': 9, 'test_metrics': {'accuracy': 0.9519, 'nll': 0.3783151762962341}, 'chosen_samples': ['20037'], 'chosen_samples_score': [1.037654495157947], 'chosen_samples_orignal_score': None})
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store['iterations'].append({'num_epochs': 15, 'test_metrics': {'accuracy': 0.9553, 'nll': 0.41963978462219237}, 'chosen_samples': ['49890'], 'chosen_samples_score': [1.2526828933252165], 'chosen_samples_orignal_score': None})
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